#coding:utf-8
 
import tensorflow as tf
import numpy as np
from PIL import Image
import mnist_backward
import mnist_forward
import os
 
def restore_model(testPicArr):
	#利用tf.Graph()复现之前定义的计算图
	with tf.Graph().as_default() as tg:
		x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
		#调用mnist_forward文件中的前向传播过程forword()函数
		y = mnist_forward.forward(x, None)
		#得到概率最大的预测值
		preValue = tf.argmax(y, 1)
 
        #实例化具有滑动平均的saver对象
		variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
		variables_to_restore = variable_averages.variables_to_restore()
		saver = tf.train.Saver(variables_to_restore)
 
		with tf.Session() as sess:
			#通过ckpt获取最新保存的模型
			ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
			if ckpt and ckpt.model_checkpoint_path:
				saver.restore(sess, ckpt.model_checkpoint_path)
		
				preValue = sess.run(preValue, feed_dict={x:testPicArr})
				return preValue
			else:
				print("No checkpoint file found")
				return -1
 
#预处理，包括resize，转变灰度图，二值化
def pre_pic(picName):
	img = Image.open(picName)
	reIm = img.resize((28,28), Image.ANTIALIAS)
	im_arr = np.array(reIm.convert('L'))
	#对图片做二值化处理（这样以滤掉噪声，另外调试中可适当调节阈值）
	threshold = 50
	#模型的要求是黑底白字，但输入的图是白底黑字，所以需要对每个像素点的值改为255减去原值以得到互补的反色。
	for i in range(28):
		for j in range(28):
			im_arr[i][j] = 255 - im_arr[i][j]
			if (im_arr[i][j] < threshold):
				im_arr[i][j] = 0
			else: im_arr[i][j] = 255
    #把图片形状拉成1行784列，并把值变为浮点型（因为要求像素点是0-1 之间的浮点数）
	nm_arr = im_arr.reshape([1, 784])
	nm_arr = nm_arr.astype(np.float32)
	#接着让现有的RGB图从0-255之间的数变为0-1之间的浮点数
	img_ready = np.multiply(nm_arr, 1.0/255.0)
 
	return img_ready
 
def application():
	for i in range(10):
		filename=str(i)+'.png'
		testPic=os.path.join('mnist_pic1',filename)
		#复制路径
		#图片预处理
		testPicArr = pre_pic(testPic)
		#获取预测结果
		preValue = restore_model(testPicArr)
		print ("The prediction number is:", preValue)
 
def main():
	application()
 
if __name__ == '__main__':
	main()		